The data used to support the findings of this study are available from the corresponding author upon request.

Although we have checked models with both H/L and G/K volatility measures, we want to make sure volatility of the short-term tenor IRS market has the same effect as the long-term tenor IRS market. << o We perform Granger causality analysis for each pair of volatility and complexity variables and discuss the results later. The AMI complexity measure has no significant correlation with the swap rate volatility but has significant negative correlation with the interest rate volatility (represented by Eurodollar futures volatility). As we mentioned earlier (see also Figure 6), we observe that the dealer network is representative of the entire swap network. The three complexity measures along with the Eurodollar futures and the swap rate volatility measures are all stationary at the ordinary level I (1) (see Table 9). doesn’t Granger-cause GnK Eurodollar futures vol. Secondly, we check the robustness of the HC complexity measure in our model for different swap tenors. In summary, our major findings are as follows. These derivative contracts, which typically exchange – or swap – fixed-rate interest payments for floating-rate interest payments, are an essential tool for investors who use them in an effort to hedge, speculate, and manage risk. The results of the bivariate Granger causality test for each pair of the Eurodollar futures volatility and complexity. First, we perform a robustness test on complexity. Copyright © 2020. We also have access to other characteristics of the swap, such as the trade date, and start and end dates of the swap, and entity classification.
We are particularly interested in the IRS market network structural features, and we aim to unravel risk-sharing behaviors of the market participants as a result of the endogeneity of the market volatility and the contract flow complexity. The first attempt to measure it was made by Luce and Perry [31]) gives an indication of the clustering in the whole network (global) and can be applied to both undirected and directed networks—often called transitivity [32]. We are committed to sharing findings related to COVID-19 as quickly as possible. does not Granger-cause MacArthur complex. Regression of unweighted network properties on the complexity measures. The interest rate risk arises because the expectation of interest rate view might not match with the actual interest rate. [29] were able to decompose MacArthur’s index into two complementary terms using the notion of conditional probability. In another word, the effective number of roles gives us a sense of risk diversification at the global level, and the effective connectivity provides an average measure of magnitude of risk positions at entity level. At the same time, we also observe 17%-18% correlation between the Eurodollar futures volatility and the swap rate volatility.

We observe some variety in terms of the daily volume of transactions, measured as the total notional. To answer this question, we use volatility data for the 1-year tenor IRS market and apply the same methodology to construct the short-term tenor market volatility using both the H/L and G/K methods. The complexity of swap network is said to Granger-cause swap rate volatility if it can be shown that the values of provide statistically significant information about the future values of . It can also be of help to understand the kind of portfolio your fund manager is holding and how over the years, he or she is trying to manage the interest rate risk in the market. And banks and swap users generally would not change their risk management strategies which should be reflected in the global diversification changes. The analyses and conclusions expressed in this paper are those of the authors and do not reflect the views of other members of the Office of Chief Economist, the Division of Enforcement, other Commission staff, or the Commission itself. This intensity increase can be interpreted as the swap users’ reaction to manage their interest rate risk by increasing their existing positions.

stream Swap is a great tool to manage your debt effectively. If we do not count the weight on the edges, the number of nodes would be 5 and the number of flows would be 7.

From these two counts, we can derive the connectivity of the network per node as 1.4, and the number of roles is 3.571. In the unweighted network, the number of roles is 3.571. We conduct the bidirectional Granger causality test for all pairs between the network complexity measures and the volatility variables. We may define them as “too-interconnected-to-fail” institutions. Neither the funding agency nor any outside organization has participated in study design or have any competing interest. Furthermore, we compare the two weighted networks in Figures 2(a) and 2(b). We can then employ impulse response analysis of the VAR model to understand the interactions between the two endogenous variables. Such a highly skewed distribution can be observed from several centrality measures, such as betweenness centrality, closeness centrality, and eigenvector centrality (see Table 4). Big investment firms, along with commercial banks that have strong credit rating history, are the largest swap market makers.

However, we are silent in interpreting how banks use Eurodollar futures and interest rate swaps to manage their risks. Figure 7 shows the impulse response simulation of a VAR model with the HC complexity and H/L swap rate volatility. The swap has allowed Mr. X a guaranteed payment of $15,000 every month. This results in 347 trading days. Essentially, we see a consistent risk shifting from bank (B) to bank (C) in Figure 2(b), while in Figure 2(a), we see risk transferring back from bank (C) to bank (B). It shows that the large swap participants are predominantly swap dealers, and their risk management behaviors determine the market overall outcome. Now when you have understood what a swap transaction is, it is very important to understand what is known as ‘swap rate.’ A swap rate is the rate of the fixed leg of the swap as determined in the free market. Hence, we arrive at the following set of effective measures for weighted networks. As we can see from the earlier discussions of the complexity measures, the overall complexity increase means the increase of either the effective number of roles (the global diversification) or the effective connectivity (the local intensity) of all the nodes. The payment for Mr. X keeps changing as the LIBOR keeps changing in the market. This finding is significant in that it suggests that the regulators can employ the AMI global diversification complexity measure to monitor market participants’ risk management behavior changes and consequently understand their global implications. Significance level code: 0.01 “. If we observe a network with γ < 2, it means that the fraction of links connected to the largest hub grows faster than the size of the network. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy, Learn from Home Offer - All in One Financial Analyst Bundle (250+ Courses, 40+ Projects) View More, Interest Rate Swap | Examples | Uses | Swap Curve, All in One Financial Analyst Bundle (250+ Courses, 40+ Projects), 250+ Courses | 40+ Projects | 1000+ Hours | Full Lifetime Access | Certificate of Completion, The trading perspective of interest rate Swap. HnL swap rate vol. Summary statistics of network properties (average of daily swap networks). It can be interpreted as the saturation of the market capacity would naturally put a break on volatility increase, and therefore, it prevents positive feedbacks between the complexity and volatility. MacArthur complex.

Boltzmann’s surprisal (s) of an event is estimated from , where is one’s surprisal at seeing an event that occurs with probability . When we perform joint regression analysis toward the market volatility, we find none of the unweighted centrality measures gives significant explanatory power. Therefore, we conclude that the model results presented in Tables 13 and 14 are representative of the entire network. Besides our swap market volatility measures, we also focus on Eurodollar futures volatility measure since both instruments are priced based on the same underlying rate, LIBOR. Now Mr. X decides that he doesn’t like this volatility and would rather have fixed interest payment, while Mr. Y decides to explore floating rate so that he has a chance of higher payments. %���� In this section, we use a unique interest rate swap transaction dataset with the counterparty details acquired from the SDRs to try to understand the interrelationship between swap market complexity and its impact on market volatility. Copyright © 2018 Steve Y. Yang and Esen Onur. Descriptive statistics of variables used. For all the price measures for both estimators, we use USD denominated fixed-float swap prices with 10-year tenor from Bloomberg, and Figure 4 shows that these two estimators result in very similar daily volatility measures. The understanding of the interest rate swap can help an investor gauge an interest rate perception in the market. Furthermore, it is reasonable to say that banks have preferential behavior when they choose counterparties. In other words, there exists a set of institutions who are both very big in terms of IRS positions and interconnectedness with the rest of the network.
However, Boltzmann’s surprisal measures the absence not the presence of an event. L1 of a time series means time lag 1 and L2 means time lag 2. The net transaction would lead Mr. Y to pay $2500 to Mr. X. Mr. X receives $20,000 from his investment at 2.00% (LIBOR standing at 1.00% and plus 1%). does not Granger-cause, GnK Eurodollar futures vol. In Table 6, we perform a set of regression analyses to show the proposed complexity measures can capture the features represented by the traditional network structural property measures. Pricing Interest Rate Swap Subject to Bilateral Counterparty Risk Xiao, Tim BMO 30 May 2019 Online at https://mpra.ub.uni-muenchen.de/94233/ MPRA Paper No. Either way, he will have a fixed monthly return of 1.5% during the tenure of the contract. does not Granger-cause GnK swap rate vol. In this section, we aim to examine the relationships between the proposed network complexity measures and the various interest rate risk measures.